Method and device for removing eeg artifacts

ABSTRACT

Systems and methods for automatically identifying segments of EEG signals or other brain electrical activity signals that contain artifacts, and/or editing the signals to remove segments that include artifacts

The present disclosure pertains to devices and methods for collectingbrain electrical activity data, and specifically to devices and methodsfor automatically editing brain electrical activity signals.

Automatic analysis of EEG data or other types of brain electricalactivity date using, for example, Quantitative Assessment of EEG,requires signals that are free of noise due to physiologic andnon-physiologic factors. Attempts at obtaining artifact-free data haveincluded methods for eliminating artifacts from EEG signals, therebyleaving only the underlying brain electrical activity signal, or byidentifying EEG segments that contain artifacts and manually editingEEGs to remove segments affected by artifact.

Current systems and methods for automatically filtering EEG signals havelimited accuracy and may not reliably identify and/or remove allartifacts. In addition, manual editing of EEGs is time consuming andsubject to user bias. Accordingly, there is a need for improved methodsfor automatically identifying EEG artifacts and editing EEGs to removesegments affected by artifacts.

A device for automatically editing brain electrical activity data isprovided. The device comprises at least two EEG electrodes; a circuitfor measuring electrical potential signals from the electrodes; a memoryunit configured to store data related to the electrical potential; ananalysis unit configured to analyze the signal to determine if temporalsegments of the signal include artifacts due to of any of eye movements,cable or electrode movements, impulse artifacts, and muscle activity,and if any segment does include artifacts, identifying the segment asincluding artifacts; and editing the data in the analysis unit to removesegments that include artifacts.

A method for automatically editing brain electrical activity data isprovided. The method comprises positioning at least two frontal EEGelectrodes on a patient; obtaining a signal representing brainelectrical activity in each of the electrodes; analyzing the signal todetermine if temporal segments of the signal include artifacts due to ofany of eye movements, cable or electrode movements, impulse artifacts,and muscle activity, and if any segment does include artifactsidentifying the segment as including artifacts; and editing the signalto remove segments that include artifacts.

DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a brain electrical activity monitoring systemaccording to one embodiment of the present disclosure.

FIG. 1B illustrates a schematic diagram of the monitoring system of FIG.1A, illustrating additional components.

FIG. 2A illustrates an electrode set for use with the brain electricalactivity monitoring system of the present disclosure.

FIG. 2B illustrates the electrode set of FIG. 1B, as applied to apatient.

FIG. 3 shows approximately eight seconds of EEG that includes artifactsproduced by vertical eye movements (VEM).

FIG. 4 shows approximately eight seconds of EEG that includes artifactsproduced by horizontal eye movements (HEM).

FIG. 5 shows approximately eight seconds of EEG that includes artifactsproduced by cable/electrode movement (PCM).

FIG. 6 shows approximately eight seconds of EEG that includes impulseartifacts (IMP).

FIG. 7 shows approximately eight seconds of EEG that includes artifactsproduced by muscle activity (EMG).

FIG. 8 shows approximately eight seconds of EEG that includes artifactsproduced by significantly low-amplitude signal due to Burst Suppression(SLAS).

FIG. 9 shows approximately eight seconds of EEG that includes AtypicalElectrical Activity Pattern (AEAP).

DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present disclosure provides devices and methods for analyzing brainelectrical activity, including editing brain electrical activity data toidentify and remove brain electrical activity signals that containcertain types of artifacts.

As used herein, “EEG signal” or “signal” refers to recordings ofcerebral electrical activity, or other types of brain electricallyactivity, recorded from any location on the cranium. EEG or other brainelectrical activity data can be stored as a digital signal in a memoryunit. As used herein, “artifacts” or “noise” refers to any electricalpotential recorded while obtaining an EEG or other brain electricalactivity signal that is not of cerebral origin or is the result ofabnormal brain activity. As used herein, “EEG electrode” refers to anyelectrode placed on a person's head and capable of detecting brainelectrical activity. EEG electrodes can be placed according to knownpositioning systems, such as, the expanded international 10/20 placementsystem. In addition, as used throughout, EEG can include cerebralelectrical activity or other types of brain electrical activity, and itwill be understood that the methods of the present disclosure can beused to identify and remove artifacts from any type of brain electricalactivity signal.

Most systems that rely on quantitative analysis of EEG typically assumethat a trained technologist has manually edited the raw data to removeartifacts. However, the editing process can be time-consuming and isinherently subjective. In addition, technologist editing preventsautomated monitoring, and therefore, is not suitable for continuous andrapid monitoring (e.g., in an ICU, in a field hospital, at a sportingevent, or in typical primary care settings). The following processingtechniques can be used to automatically identify and/or remove (e.g.,edit out) EEG or other brain electrical activity data segments thatinclude artifacts. This may be accomplished using standard signalprocessing components, which include digital filtering (low-passfiltering, bandpass filtering, etc.), thresholding, peak detection, andfrequency-based processing.

There are seven typical types of noise that can contribute to poorsignal quality. EEG segments including each of these types of artifacts,as recorded with a limited electrode montage (i.e., 5 electrodes) areshown in FIGS. 3-9, with the segment containing artifact data identifiedby a dark dashed-line box. These EEGs were recorded with electrodeimpedances under 5 kn. The data was sampled at 8 kHz, low-pass filteredto remove signal frequencies above 45 Hz, and downsampled to 100 Hz forpurposes of display and editing. These artifacts include (1)horizontal/lateral eye movements (HEM) (see FIG. 3, 300), (2) verticaleye movements (e.g. blinks) (VEM) (see FIG. 4, 400), (3) cable orelectrode movement causing over-range artifacts (PCM) (see FIG. 5, 500),(4) impulse artifacts (for example due to electrode “pops”) (IMP) (seeFIG. 6, 600), (5) electromyographic activity (also referred to as“muscle activity”) (EMG) (see FIG. 7, 700), (6) significantly lowamplitude signal (for example as a result of the suppression componentof “burst suppression”) (SLAS) (see FIG. 8, 800), and (7) atypicalelectrical activity pattern (for example due to paroxysmal brainactivity) (AEAP) (see FIG. 9, 900). Out of these seven artifact types,two are non-physiological (type 3, type 4), three are physiological, butare not brain-generated (type 1, 2, type 5) and two are brain-generated(type 6, type 7). All of these artifacts reflect either non-brainelectrical activity or abnormal brain-electrical activity.

The present disclosure provides a comprehensive, fully-automated,artifact detection system, mimicking the ability of trained EEGtechnologists to edit EEG records. The edited records may be used forsubsequent processing and analysis, using, for example, quantitativeanalyses of brain electrical activity. In certain embodiments, themethod and device of the present disclosure can include a limitedfrontal electrode montage, as described further below.

In certain embodiments, the present disclosure provides a device andmethod for automatically editing EEG signals. In certain embodiments,the method comprises positioning at least two frontal EEG electrodes ona patient, and obtaining a signal representing brain electrical activityin each of the electrodes. The signal can be analyzed to determine iftemporal segments of the signal include artifacts due to of any of eyemovements, cable or electrode movements, impulse artifacts, and muscleactivity, and if any segment does include artifacts identifying thesegment as including artifacts. In some embodiments, the signal isedited to remove segments that include artifacts. In some embodiments,the method further includes analyzing the signal to determine iftemporal segments of the signal include artifacts due to of anysignificantly low amplitude signal and atypical electrical activity.

A number of different EEG systems can be used to collect data using themethods of the present disclosure. In certain embodiments, the systemcan be a compact, self-contained device. For example, FIG. 1Aillustrates an EEG system 10, according to certain embodiments of thepresent disclosure. As shown, the system 10 can include an enclosure 20containing electrical circuitry configured to perform data processing,stimulus generation, and analysis for diagnosis and patient monitoring.In addition, the enclosure 20 may further include a display system 30,such as an LCD or other visual display to provide real-time,easy-to-interpret information related to a patient's clinical status.

In some embodiments, the system 10 will include circuitry configured toprovide real-time monitoring of brain electrical activity. The system 10will provide rapid data acquisition, processing, and analysis to allowpoint-of-care diagnosis and assessment. For example, as shown, thedisplay system 30 can include one or more indicators 35, or visualdisplays, that are configured to display an easy-to-interpret indicationof a patient's status. In one embodiment, the indicators 35 will includean indication of where a patient's status lies relative to a normal dataset, a patient's status relative to a base line, and/or one or moreindicators of the origin of any abnormalities. In some embodiments, theindicators provide a scale (from normal to severely abnormal). In otherembodiments, typical EEGs, as shown in the attached figures may bedisplayed on the system 30, as recorded and/or after editing.

FIG. 1B illustrates a schematic diagram of the monitoring system of FIG.1A, illustrating additional components. As shown, the enclosure 20, caninclude a number of component parts. For example, the enclosure 20 mayinclude a memory unit or storage system 22 configured to store datarelated to patient brain electrical activity data measurements, or adatabase of normal and/or pathological readings. Further, the enclosurewill include circuitry configured to process and evaluate electricalsignals and data 24, and a transmitter unit 26.

The circuitry 24 can include a number of circuitry types. For examplethe circuitry 24 can include processing circuitry configured to receiveelectrical signals from electrodes and to process such signal usingfilters (e.g., band pass, low pass, and/or high pass filters), as shownin FIGS. 2A-2B, and to convert such signals into data that can befurther evaluated. In some embodiments, the circuitry can be configuredto enable nonlinear processing, including nonlinear amplifiers. Further,the circuitry 24 can also include components configured to allowanalysis of processed data and comparison of brain electrical activitydata to normal data, or to previous or future measurements, as describedin more detail below. Further, it will be understood that, althoughshown as a single component, multiple components can be included, eitheron a single chip or multiple chips.

The transmitter unit 26 can include a number of transmitter types. Forexample, the transmitter 26 may include a hardware connection for acable or a telemetry system configured to transmit data to a moredistant receiver 28, or a more powerful transmission system to redirectdata to a database 32 that may be stored nearby or at a remote ordistant location. In certain embodiments, the data can be transmittedand stored and/or evaluated at a location other than where it iscollected.

The brain electrical monitoring system 10 may be configured to attach tovarious patient interfaces. For example, FIGS. 2A-2B illustrate anelectrode set 50 for use with the system 10 of the present disclosure.As shown, the electrode set 50 includes one or more electrodes 60 forplacement along the patient's forehead and mastoid region. As shown, theelectrode set 50 includes a limited number of electrodes 60 tofacilitate rapid and easily repeated placement of the electrodes 60 forefficient, but accurate, patient monitoring. Further, in one embodiment,the electrodes 60 may be positioned on a head band 70 that is configuredfor easy and/or rapid placement on a patient, as shown in FIG. 2B.Further, it will be understood that other electrode configurations maybe selected, which may include fewer or more electrodes.

In certain embodiments, a limited frontal electrode montage can be usedto implement the methods of the present disclosure, including at leasttwo electrodes. In some embodiments, the at least two frontal EEGelectrodes are positioned at FP1 and FP2 positions based on the expandedinternational 10/20 placement system. In some embodiments, the at leasttwo frontal EEG electrodes are positioned at F7 and F8 positions basedon the expanded international 10/20 placement system. In someembodiments, the electrodes include at least five electrodes positionedat FP1, FP2, F7, F8, and AFz positions based on the expandedinternational 10/20 placement system.

As noted, the electrode set 50 will be operably connected to themonitoring system 10. Generally, the electrodes 60 will be electricallycoupled with the monitoring system 10 to allow signals received from theelectrodes to be transmitted to the monitoring system 10. Such anelectrical coupling will generally be through one or more electricalwires, but nonphysical connections may also be used.

To identify artifacts, EEG signals may be analyzed in certain temporalsegments or epochs. Generally, the segment duration should be longenough to allow identification of artifacts in question, but as short aspossible to minimize editing out segments or data that do not containartifact. In some embodiments, segments having lengths between 10 to 500ms are analyzed and/or edited out if they contain EEG artifacts. In oneembodiment, the signals are analyzed in approximately 320 ms lengthsegments or sub-epochs, although other signal lengths may be useddepending, for example, on the type of brain electrical activity beinganalyzed. In certain embodiments, when editing out segments containingartifacts, data recorded just before and/or after the artifacts may alsobe edited out. For example, in some embodiments, segments of duration of320 ms occurring immediately before and immediately after a segmentcontaining artifact are automatically edited out.

In certain embodiments, slow lateral eye movements (HEMs) areidentified, and brain electrical activity data segments containinglateral eye movement artifacts are edited out of the signal. In certainembodiments, HEM artifacts are identified as waveforms of 1 Hz or lessthat have opposite polarity at F7 and F8. Each of the two EEG channelsF7 and F8 may band-pass filtered using an FIR filter with passband 0.5-3Hz, producing signals F7 f and F8 f, the high-pass cut-off frequency of0.5 Hz being chosen to ignore the influence of low-frequency activityoccurring at frequencies below the delta_(—)1 band (0.5-1.5 Hz). EEGsegments containing HEM artifacts are identified wherever the differencesignal F7 f-F8 f exceeds a threshold. In various embodiments, thethreshold can be between 10 μV and 100 μV, or between 10 μV and 30 μV,or in one embodiment, approximately 24 μV.

In certain embodiments, vertical eye movement (VEM)/eye opening/eyeclosing (EOEC) artifacts are identified, and brain electrical activitydata segments containing those artifacts are edited out of the signal.Detection of the electrophysiological effect of a vertical eye movement(VEM) (of which eye opening/closing is a sub-type) can performed bylocating large excursions (“peaks”) on the Fp1 and Fp2 leads. Since botheyes move in unison, only such excursions that occur concurrently and inthe same direction (same polarity of the peaks) on Fp1 and Fp2 areidentified as vertical eye movements. In some embodiments, each of thetwo signals Fp1 and Fp2 is first low-pass filtered in the range 0.5-5Hz. In each segment, runs of samples exceeding a given threshold. Invarious embodiments, the threshold can be between 10 μV and 100 μV, orbetween 10 μV and 30 μV, or in one embodiment, approximately 24 μV. Ineach such run, the global extremum is located and its value is comparedto average signal values on either side of it. If the absolutedifference between the extremum and either average exceeds thethreshold, the segment is identified as a candidate VEM artifact. Afterthis processing has occurred on both leads, the results are combined toturn candidate VEMs to true VEMs wherever they occurred concurrently onFp1 and Fp2 as described above. In certain embodiments, determining iftemporal segments of the signal include artifacts due to eye movementsincludes filtering signals obtained from electrodes positioned at theFp1 and Fp2 positions, comparing each signal to an average signal fromthe same electrode, determining if the signals exceed a threshold, andif the signal exceeds a threshold, determining if changes in the Fp1 andFp2 signals occur concurrently, and if the changes do occurconcurrently, identifying the segment as including artifacts.

In some embodiments, cable or electrode movement (PCM) artifacts, areidentified, and brain electrical activity data segments containing thoseartifacts are edited out of the signal. In some embodiments, determiningif temporal segments of the signal include artifacts due to cable orelectrode movement includes identifying a signal amplitude greater thana threshold, and if any segment includes an amplitude greater than thethreshold, identifying that segment as including cable or electrodemovement artifacts. In certain embodiments, the threshold can be between50 μV and 250 μV, or between 50 μV and 150 μV, or, in one embodiment,approximately 120 μV.

In some embodiments, impulse artifacts are identified, and brainelectrical activity data segments containing those artifacts are editedout of the signal. In some embodiments, a frontal EEG channel is firsthigh-pass filtered with cutoff frequency at to remove the alpha-1 bandfrom the signal in that channel. In some embodiments, the cut-offfrequency is 15 Hz. Next, high-frequency variations of signal amplitudein successive segments of 100 ms width with 50% overlap are examined.Within each segment, the value (max-min) is computed and trigger an IMPartifact detection when it exceeds a given threshold. In certainembodiments, the threshold can be between 25 μV and 250 μV, or between50 μV and 125 μV, or in one embodiment, approximately 75 μV.

In some embodiments, muscle activity (EMG) artifacts are identified, andbrain electrical activity data segments containing those artifacts areedited out of the signal. This artifact is characterized byhigh-frequency signals (above 20 Hz) occurring in bursts of variableduration. In certain embodiments, muscle movement artifacts areidentified by band pass filtering a signal from at least one electrodein the range of the EEG β1 band to produce signal E1 and band passfiltering the same signal in the range of the β2 band to produce signalE2, and if relative energy of E2 relative to E1 exceeds a threshold,identifying the segment as containing muscle movement artifacts. Incertain embodiments, the signal is band-pass filtered in the range of25-35 Hz (β2 band) and 15-25 Hz (β1 band).

In some embodiments, brain electrical activity data segments containingsignificantly low amplitude signal (SLAS) are identified, and brainelectrical activity data segments containing those artifacts are editedout of the signal. This artifact is meant to capture extremelylow-amplitude EEG signals (at all frequencies) which occur, for example,when the brain is in Burst Suppression mode; a condition which can occur(but should be avoided) during anesthesia. No additional filtering ofthe signal is used for detection of this activity. In some embodiments,SLAS can be detected by looking for signal epochs with mean-squareenergy below a threshold. In certain embodiments, the threshold can bebetween 1 μV² and 25 μV², or between 10 μV² and 15 μV², or, in oneembodiment, approximately 12 μV².

In some embodiments, brain electrical activity data segments containingatypical electrical activity pattern (AEAP) are identified, and brainelectrical activity data segments containing those artifacts are editedout of the signal. This artifact includes unusual patterns of activityin the signal such as those that occur in the EEG of epileptic subjectsduring a convulsive or non-convulsive seizure. Such artifacts can beidentified using a combination of wavelet analysis and fractal dimensioncomputation, as described in A. Jacquin et al. “Automatic Identificationof Spike-Wave Events and Non-Convulsive Seizures with a Reduced Set ofElectrodes,” Proceedings of the 29th IEEE EMBS International Conference,Lyon, France, August 2007.

The methods for automatically identifying and editing out brainelectrical activity data segments that contain artifacts has been testedand validated by comparison to manual editing techniques. The processhas been found to be suitable for editing recordings from patients witha variety of different pathologies, including, for example, traumaticbrain injury with positive imaging, head injuries/concussions withnegative or no imaging, subjects who had no head injury or evidence ofCNS abnormalities, subjects with strokes or tumors, subjects withalcohol or drug encephalopathies, and other patient populations. Inaddition, the methods have been used to edit EEG recordings frompatients with cerebro-vascular accidents (CVA) who frequently had thecharacteristic of frontal slow waves in their EEGs, indicating that themethods of the present disclosure remove pathology from the EEG bymistaking it as artifact.

Other embodiments will be apparent to those skilled in the art fromconsideration of the specification and practice of the devices andmethods disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope beingindicated by the following claims.

1. A method for automatically editing brain electrical activity data,comprising: positioning at least two frontal EEG electrodes on apatient; obtaining a signal representing brain electrical activity ineach of the electrodes; analyzing the signal to determine if temporalsegments of the signal include artifacts due to of any of eye movements,cable or electrode movements, impulse artifacts, and muscle activity,and if any segment does include artifacts identifying the segment asincluding artifacts; and editing the signal to remove segments thatinclude artifacts.
 2. The method of claim 1, wherein the at least twofrontal EEG electrodes are positioned at FP1 and FP2 positions based onthe expanded international 10/20 placement system.
 3. The method ofclaim 1, wherein the at least two frontal EEG electrodes are positionedat F7 and F8 positions based on the expanded international 10/20placement system.
 4. The method of claim 1, wherein the at least twofrontal EEG electrodes are positioned at FP1 and FP2 positions based onthe expanded international 10/20 placement system.
 5. The method ofclaim 1, wherein the electrodes include at least five electrodespositioned at FP1, FP2, F7, F8, and AFz positions based on the expandedinternational 10/20 placement system.
 6. The method of claim 1, furtherincluding analyzing the signal to determine if temporal segments of thesignal include artifacts due to of any significantly low amplitudesignal and atypical electrical activity.
 7. The method of claim 6,wherein the electrodes include at least five electrodes positioned atFP1, FP2, F7, F8, and AFz positions based on the expanded international10/20 placement system.
 8. The method of claim 7, wherein determining iftemporal segments of the signal include artifacts due to eye movementsincludes filtering signals obtained from electrodes positioned at F7 andF8 positions and determining if the difference between the filteredsignals exceeds a threshold.
 9. The method of claim 7, whereindetermining if temporal segments of the signal include artifacts due toeye movements includes filtering signals obtained from electrodespositioned at the Fp1 and Fp2 positions, comparing each signal to anaverage signal from the same electrode, determining if the signalsexceed a threshold, and if the signal exceeds a threshold, determiningif changes in the Fp1 and Fp2 signals occur concurrently, and if thechanges do occur concurrently, identifying the segment as includingartifacts.
 10. The method of claim 1, wherein determining if temporalsegments of the signal include artifacts due to cable or electrodemovement includes identifying a signal amplitude greater than apredetermined threshold, and if any segment includes an amplitudegreater than the threshold identifying that segment as including cableor electrode movement artifacts.
 11. The method of claim 10, wherein thethreshold is between 50 and 250 μV.
 12. The method of claim 7, whereindetermining if temporal segments of the signal include impulse artifactsincludes high pass filtering a signal from at least one electrode toremove the alpha component of the signal and determining if successive100 ms segments include signal variations greater than a predeterminedthreshold, and if is successive 100 ms segments include signalvariations of greater than the threshold, identifying the segments ascontaining impulse artifacts.
 13. The method of claim 12, wherein thethreshold is between 25 and 250 μV.
 14. The method of claim 7, whereindetermining if temporal segments of the signal include muscle movementartifacts includes band pass filtering a signal from at least oneelectrode in the range of the EEG β1 band to produce signal E1 and bandpass filtering the same signal in the range of the β2 band to producesignal E2, and if relative energy of E2 relative to E1 exceeds athreshold, identifying the segment as containing muscle movementartifacts.
 15. The method of claim 7, wherein determining if temporalsegments of the signal include significantly low amplitude signalincludes determining if the mean square energy of a segment is below athreshold, and if the mean square energy is below a threshold,identifying the segment as including significantly low amplitude signal.16.-28. (canceled)
 29. A device for automatically editing brainelectrical activity data signals, comprising: at least two electrodes; acircuit for measuring electrical potential signals from the electrodes;a memory unit configured to store data related to the electricalpotential; an analysis unit configured to analyze the signal todetermine if temporal segments of the signal include artifacts due to ofany of eye movements, cable or electrode movements, impulse artifacts,and muscle activity, and if any segment does include artifactsidentifying the segment as including artifacts; and editing the data inthe analysis unit to remove segments that include artifacts.
 30. Thedevice of claim 29, wherein the at least two EEG electrodes arepositioned at FP1 and FP2 positions based on the expanded international10/20 placement system.
 31. The device of claim 29, wherein the at leasttwo EEG electrodes are positioned at F7 and F8 positions based on theexpanded international 10/20 placement system.
 32. The device of claim29, wherein the at least two EEG electrodes are positioned at FP1 andFP2 positions based on the expanded international 10/20 placementsystem.
 33. The device of claim 29, wherein the electrodes include atleast five electrodes positioned at FP1, FP2, F7, F8, and AFz positionsbased on the expanded international 10/20 placement system.
 34. Thedevice of claim 29, wherein the analysis unit is configured to analyzethe signal to determine if temporal segments of the signal includeartifacts due to of any significantly low amplitude signal and atypicalelectrical activity.
 35. The device of claim 34, wherein the electrodesinclude at least five electrodes positioned at FP1, FP2, F7, F8, and AFzpositions based on the expanded international 10/20 placement system.36. The device of claim 35, wherein determining if temporal segments ofthe signal include artifacts due to eye movements includes filteringsignals obtained from electrodes positioned at F7 and F8 positions anddetermining if the difference between the filtered signals exceeds athreshold.
 37. The device of claim 35, wherein determining if temporalsegments of the signal include artifacts due to eye movements includesfiltering signals obtained from electrodes positioned at the Fp1 and Fp2positions, comparing each signal to an average signal from the sameelectrode, determining if the signals exceed a threshold, and if thesignal exceeds a threshold, determining if changes in the Fp1 and Fp2signals occur concurrently, and if the changes do occur concurrently,identifying the segment as including artifacts.
 38. The device of claim29, wherein determining if temporal segments of the signal includeartifacts due to cable or electrode movement includes identifying asignal amplitude greater than a predetermined threshold, and if anysegment includes an amplitude greater than the threshold, identifyingthat segment as including cable or electrode movement artifacts.
 39. Thedevice of claim 35, wherein determining if temporal segments of thesignal include impulse artifacts includes high pass filtering a signalfrom at least one electrode to remove the alpha component of the signaland determining if successive 100 ms segments include signal variationsof greater than a predetermined threshold, and if is successive 100 mssegments include signal variations of greater than the threshold,identifying the segments as containing impulse artifacts.
 40. The deviceof claim 35, wherein determining if temporal segments of the signalinclude muscle movement artifacts includes band pass filtering a signalfrom at least one electrode in the range of the EEG β1 band to producesignal E1 and band pass filtering the same signal in the range of the β2band to produce signal E2, and if relative energy of E2 relative to E1exceeds a threshold, identifying the segment as containing musclemovement artifacts.
 41. The device of claim 35, wherein determining iftemporal segments of the signal include significantly low amplitudesignal includes determining if the mean square energy of a segment isbelow a threshold, and if the mean square energy is below a threshold,identifying the segment as including significantly low amplitude signal.